text: 
  from caffe import layers as L, params as P, to_proto
  from caffe.proto import caffe_pb2
  
  # helper function for common structures
  
  def conv_relu(bottom, ks, nout, stride=1, pad=0, group=1):
      conv = L.Convolution(bottom, kernel_size=ks, stride=stride,
                                  num_output=nout, pad=pad, group=group)
      return conv, L.ReLU(conv, in_place=True)
  
  def fc_relu(bottom, nout):
      fc = L.InnerProduct(bottom, num_output=nout)
      return fc, L.ReLU(fc, in_place=True)
  
  def max_pool(bottom, ks, stride=1):
      return L.Pooling(bottom, pool=P.Pooling.MAX, kernel_size=ks, stride=stride)
  
  def caffenet(lmdb, batch_size=256, include_acc=False):
      data, label = L.Data(source=lmdb, backend=P.Data.LMDB, batch_size=batch_size, ntop=2,
          transform_param=dict(crop_size=227, mean_value=[104, 117, 123], mirror=True))
  
      # the net itself
      conv1, relu1 = conv_relu(data, 11, 96, stride=4)
      pool1 = max_pool(relu1, 3, stride=2)
      norm1 = L.LRN(pool1, local_size=5, alpha=1e-4, beta=0.75)
      conv2, relu2 = conv_relu(norm1, 5, 256, pad=2, group=2)
      pool2 = max_pool(relu2, 3, stride=2)
      norm2 = L.LRN(pool2, local_size=5, alpha=1e-4, beta=0.75)
      conv3, relu3 = conv_relu(norm2, 3, 384, pad=1)
      conv4, relu4 = conv_relu(relu3, 3, 384, pad=1, group=2)
      conv5, relu5 = conv_relu(relu4, 3, 256, pad=1, group=2)
      pool5 = max_pool(relu5, 3, stride=2)
      fc6, relu6 = fc_relu(pool5, 4096)
      drop6 = L.Dropout(relu6, in_place=True)
      fc7, relu7 = fc_relu(drop6, 4096)
      drop7 = L.Dropout(relu7, in_place=True)
      fc8 = L.InnerProduct(drop7, num_output=1000)
      loss = L.SoftmaxWithLoss(fc8, label)
  
      if include_acc:
          acc = L.Accuracy(fc8, label)
          return to_proto(loss, acc)
      else:
          return to_proto(loss)
  
  def make_net():
      with open('train.prototxt', 'w') as f:
          print(caffenet('/path/to/caffe-train-lmdb'), file=f)
  
      with open('test.prototxt', 'w') as f:
          print(caffenet('/path/to/caffe-val-lmdb', batch_size=50, include_acc=True), file=f)
  
  if __name__ == '__main__':
      make_net()
  